US20190385752A1 - Automated provisioning of clinical advice associated with a patient with diabetes (pwd) and related systems, methods, and devices - Google Patents

Abstract

A computer-implemented method for automated provisioning of clinical advice to a provider caring for a patient with diabetes (PWD). The method includes detecting a clinically relevant pattern in therapy data of a patient under the care of a provider, and identifying a predefined behavior of a PWD responsive to the detected clinically relevant pattern. The clinically relevant detected pattern is indicative of desirable behaviors and/or undesirable behaviors. The method further includes selecting a therapy insight associated with the identified predefined behavior, wherein the therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation, and responsive to the selecting the therapy insight, automatically sending, to a provider-dashboard associated with the provider caring for the PWD, the selected therapy insight.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS
• This application claims the benefit of the filing date of U.S. Provisional Patent Application Ser. No. 62/790,405, filed Jan. 9, 2019, for “Clinical Decision Support System, and Related Systems, Methods, and Devices,” the contents and disclosure of which is hereby incorporated herein in its entirety by this reference. This application also claims the benefit of the filing date of U.S. Provisional Patent Application Ser. No. 62/686,556, filed Jun. 18, 2018, for “Remote Monitoring Tools for Diabetes Management Systems, and Related Methods, Systems and Devices,” the contents and disclosure of which is hereby incorporated herein in its entirety by this reference.
TECHNICAL FIELD
• This disclosure relates to automated provisioning of clinical advice systems, methods, and devices adapted to collect and/or transmit data relating to clinical advice associated with a person with diabetes (PWD) and/or other therapy related data and to provide a user with therapy recommendations. In particular embodiments, automated provisioning of clinical advice systems, devices, and methods are disclosed, which may be utilized with insulin injection devices, including components adapted to provide a user with therapy insights based, at least in part on, identified predefined behaviors.
BACKGROUND
• Diabetes mellitus is a chronic metabolic disorder caused by the inability of a person's pancreas to produce sufficient amounts of the hormone insulin such that the person's metabolism is unable to provide for the proper absorption of sugar and starch. The inability to absorb those carbohydrates sometimes leads to hyperglycemia, i.e., the presence of an excessive amount of glucose within the blood plasma. Hyperglycemia has been associated with a variety of serious symptoms and life threatening long-term complications such as dehydration, ketoacidosis, diabetic coma, cardiovascular diseases, chronic renal failure, retinal damage and nerve damages with the risk of amputation of extremities.
• Because healing is not yet possible, a permanent therapy is necessary which maintains a proper blood glucose level within normal limits. Maintaining a proper glucose level is achieved by regularly supplying insulin to a person with diabetes (PWD). Maintaining a proper blood glucose level creates a significant cognitive burden for a PWD and affects many aspects of the PWD's life. For example, the cognitive burden on a PWD can be attributed to, among other things, tracking meals and constant check-ins and minor course corrections of blood glucose levels. The adjustments of blood glucose levels by a PWD can include taking insulin, tracking insulin dosing and glucose, deciding how much insulin to take, how often to take it and how to time insulin doses in relation to meals and/or glucose fluctuations. These factors make up just a portion of the significant cognitive burden of a PWD.
• The following example of a typical daily routine for a PWD further illustrates the significant cognitive burden of a PWD. In the morning, the first thoughts/actions by a PWD is often related to their glucose, such as, what is their blood glucose level? How was their blood glucose level overnight? And how are they currently feeling? Upon checking their blood glucose levels (e.g., using a blood glucose meter or monitor), a PWD can then consider what actions to take, such as adjusting their morning activities, changing when or what to eat for breakfast, or determining to take rapid-acting (RA) insulin. Before they even eat breakfast (or any meal), a PWD considers the amount of food and types of food they plan to eat, perhaps modifying their RA insulin dose based on the carbohydrate content of the food they choose to eat. Before they administer RA insulin, the PWD will try to remember when they took their last dose of insulin, what happened the last time they ate a particular meal and how they felt.
• Before leaving the house, a PWD considers, among other things, whether they have enough supplies for glucose monitoring or insulin dosing. This can include batteries, charged devices, backup supplies, glucose testing supplies, and insulin supplies to treat for high blood glucose levels. Additionally, a PWD needs to consider any physical activities (e.g., walking kids to school, going to the gym, riding a bike) that will affect their glucose because exercise may cause their blood glucose to go lower than expected. Even before driving a vehicle, a PWD checks their glucose to determine if it is at a safe level for driving.
• As lunchtime approaches, a PWD considers their glucose prior to eating lunch, such as what time they can expect to eat, what they expect to eat throughout the day. As such, a PWD tallies up the carbohydrates and adjusts insulin doses in their head. A PWD also considers what insulin doses were recently taken and whether those doses may still be working to lower blood glucose. This is all done in parallel with whatever they are doing in their busy day, and so the PWD often forgets or fails to fully consider all of the factors described above.
• Throughout the day, a PWD often checks glucose levels, especially on days when their activities vary from a typical day. This constant thinking, checking, planning can be exhausting, especially when each check requires decisions, math, and possible behavior changes. Additionally, during the day, a PWD may check inventory on supplies, speak with a health care provider (HCP), refill prescriptions, contact their health insurance to discuss their therapy and/or supplies.
• In the evening, after an exhausting day, a PWD may have to take a daily insulin dose of long-acting (LA) insulin. Additionally, the PWD may determine if their glucose is holding steady before they fall asleep. If they use an infusion pump, they have to check if their insulin pump is low on insulin and whether they need to refill it before sleep. If they have a continuous glucose monitor, they have to check and see if it is working. Even then, based on what they ate for dinner, the nighttime insulin might not keep their glucose steady. Glucose levels in the night can interfere with sleep as well as add anxiety that could disrupt sleep.
• Accordingly, managing diabetes requires significant attention to detail throughout the day. Even with careful planning and self-monitoring, a PWD may skip doses, double dose, or dose the wrong amount and/or type of insulin. Insufficient insulin can result in hyperglycemia, and too much insulin can result in hypoglycemia, which can result in clumsiness, trouble talking, confusion, loss of consciousness, seizures, or death.
• In order to assist with self-treatment, some diabetes treatment devices (e.g., blood glucose meters, insulin pumps, etc.) are equipped with insulin bolus calculators that have the user input an estimate (e.g., numerical estimate) of the quantity of carbohydrates consumed or about to be consumed (or additionally or alternatively protein, fat, or other meal data) and the bolus calculator outputs a recommended size for the insulin bolus dosage. Although bolus calculators remove some of the mental calculations that need to be made by the user in determining an appropriate insulin bolus dosage, bolus calculators still burden the user with the mental task of evaluating the constituents of their meal, may require the use of a secondary device, and often require manual entry of data.
• A healthcare provider (HCP) (e.g., physician, endocrinologist) may assist the PWD in the self-treatment. For example, an HCP may assist the PWD via a dosing system used to connect PWDs with HCPs to improve awareness and knowledge with the goal to ultimately improve insulin therapy outcomes. Some conventional dosing systems provide recommendations to PWDs and HCPs for updating and changing insulin delivery settings and/or track blood glucose patterns, carbohydrate intake, and exercise, and provide summary information of the same. Some conventional dosing systems focus ontype 2 diabetes with an emphasis on only basal rate, requesting food logging, requesting exercise logging, and information about past blood glucose highs and lows. However, some conventional dosing systems do not provide recommendations for improving or updating PWD behavior or therapy settings. Moreover, none focus on the ability to set an initial therapy recommendation and allow updating of the therapy recommendation. Additionally, some conventional dosing systems do not provide options for changing incorrect behaviors, nor do they provide recommendations for continuing correct behaviors; link patterns of behavior to specific types of dosing, more specifically, identify a behavior pattern and link it to long acting insulin doses or rapid acting insulin doses; or track whether a dose was given for multiple daily injections (MDI) or if the wrong dose was given.
• Although conventional dosing systems may remove some of the mental burdens for the HCP and/or PWD in determining an appropriate recommendation related to insulin dosing, dosing systems still burden the HCP and/or PWD with the mental task of at least manually evaluating therapy data, manually determining a dosing recommendation, and often require manual entry of data.
BRIEF DESCRIPTION OF THE DRAWINGS
• The present disclosure may be understood more fully by reference to the following detailed description of example embodiments, which are illustrated in the accompanying figures.
• FIG. 1A illustrates a clinical decision support (CDS) system according to embodiments of the present disclosure.
• FIG. 1B illustrates a CDS system according to embodiments of the present disclosure.
• FIG. 2A illustrates a list of patients presented to an HCP on HCP dashboard according to embodiments of the present disclosure.
• FIG. 2B illustrates an overview of an insight presented to an HCP on HCP dashboard according to embodiments of the present disclosure.
• FIG. 2C illustrates a glucose data view of an insight according to embodiments of the present disclosure.
• FIG. 2D illustrates a statistics and insights view of an insight according to embodiments of the present disclosure.
• FIG. 2E illustrates a settings view of an insight according to embodiments of the present disclosure.
• FIG. 2F illustrates a recommendation history view of an insight according to embodiments of the present disclosure.
• FIG. 3 illustrates workflow for selecting and sending recommendations to a PWD via HCP dashboard according to embodiments of the present disclosure.
• FIG. 4 illustrates a view of various information associated with recommendations, therapy changes and/or care notes using an HCP dashboard according to embodiments of the present disclosure.
• FIG. 5 illustrates an insight view displayed on an HCP dashboard according to an embodiment of the present disclosure.
• FIG. 6 illustrates an insight view displayed on an HCP dashboard according to an embodiment of the present disclosure.
• FIG. 7A illustrates a summary view presented at a PWD dashboard according to embodiments of the present disclosure.
• FIG. 7B illustrates a view presented at a PWD dashboard according to embodiments of the present disclosure.
• FIG. 7C illustrates a summary view of recommended therapy setting updates presented at a PWD dashboard according to embodiments of the present disclosure.
• FIG. 7D illustrates a view of training modules presented at a PWD dashboard according to embodiments of the present disclosure.
• FIG. 8 illustrates a method for automated provisioning of clinical advice to a provider caring for a PWD according to embodiments of the present disclosure.
• FIG. 9 illustrates a method of accepting a recommendation by a PWD according to embodiments of the present disclosure.
• FIG. 10 illustrates a therapy management system according to embodiments of the present disclosure.
• FIG. 11A illustrates a therapy management system according to embodiments of the present disclosure.
• FIG. 11B illustrates a therapy management system according to embodiments of the present disclosure.
• FIG. 12 illustrates a method for managing therapy settings for a PWD according to embodiments of the present disclosure.
• FIG. 13 illustrates a method for managing therapy settings for a PWD according to embodiments of the present disclosure.
DETAILED DESCRIPTION
• Improved systems for communication between HCPs and PWDs would reduce some of the cognitive burden on the PWD to manage their diabetes. A limiting factor is that HCPs and PWDs do not have a system that lets them quickly and efficiently identify therapy trends or behavior trends from therapy data and then communicate therapy or behavior recommendations to a patient.
• Various embodiments disclosed herein relate to clinical decision support systems configured to automatically analyzing therapy data, identifying insights about a patient's therapy, and providing those insights to an HCP so that an HCP can make therapy and/or behavior recommendations to the patient in a timely, contextually relevant manner. In one embodiment, content for a recommendation based on insight may also be automatically provided to an HCP, and a user interface may be provided for reviewing insights, selecting content for recommendations, editing recommendations, and sending recommendations to a PWD.
• Various embodiments described herein relate, generally, to systems and interfaces for HCPs to update therapy and behavioral recommendations for a PWD that are provided by clinical decision support systems. In various embodiments, the system and interface simplifies the workflows of HCPs by providing a preset list of insights to the HCP. In various embodiments, insights are trends that are observed using blood glucose data and/or therapy data, and which may be characterized, and/or translated into actionable and causational messages which are sent to a PWD. An HCP may use the interfaces disclosed herein to research insights, and determine which insights are relevant to send to a PWD. In some embodiments, the interfaces disclosed herein enable an HCP to receive or have access to a status of such insight being accepted or rejected by the PWD. In some embodiments, interfaces disclosed herein enable an HCP to access supporting data for insights highlighted within various reports to guide an HCP in clinical decision support. In some embodiments, parameters of insights may be configured by an HCP. For example, an HCP can revise an insight (by changing parameters of the insight) based, at least in part, on PWD feedback and/or motivation.
• As already mentioned, various embodiments described herein relate, generally, to systems and methods for providing clinical decision support, and more specifically, to supporting HCPs in improving health outcomes for treatment of PWDs. Examples of improving health outcomes may include improving time within a target glucose range, reducing number and/or severity of episodes of hypoglycemia and diabetic ketoacidosis (DKA), and more generally, reducing risk of long-term complications. Examples of support for HCPs may include reducing time, effort, and/or expertise required by a provider to support a patient and/or therapy management systems. Additionally, support for HCPs may include standardization of care. For example, HPCs oftentimes interpret raw data differently and potentially lead to different therapy solutions. The preset insights will alleviate this issues and lead to a more consistent and standardized care for PWDs.
• As used herein, the term “insight” means a recognition of a behavioral or therapy related trend or pattern that is clinically relevant to a PWD. One or more behavioral or therapy recommendations may be associated with an insight. Behavioral recommendations are recommendations for behaviors that, when implemented by a PWD, are associated with improved therapy outcomes for the PWD (e.g., improving time within a target glucose range, reducing number and/or severity of episodes of hypoglycemia and diabetic ketoacidosis (DKA), and more generally, reducing risk of long-term complications).
• FIG. 1A illustrates a block diagram of a clinical decision support (CDS)system100A, in accordance with one or more embodiments of the disclosure. In some embodiments,CDS system100A may include one or more servers, and the servers may be configured to communicate with one or more client computing platforms according to a client/server architecture and/or other architectures. Client computing platform(s) may be configured to communicate with other client computing platforms via server(s) and/or according to a peer-to-peer architecture and/or other architectures. Users may also accessCDS system100A via client computing platform(s).
• Embodiments ofCDS system100A, and methods of configuring and operatingCDS system100A, may be performed, in whole or in part, in cloud computing, client-server, or other networked environment, or any combination thereof. The components of such a system may be located in a singular “cloud” or network, or spread among many clouds or networks. End-user knowledge of a physical location and/or configuration of components of a system are not required.
• In some embodiments, server(s), client computing platform(s), and/or various external resources may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes embodiments in which server(s), client computing platform(s), and/or external resources may be operatively linked via some other communication media.
• A given client computing platform may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable an expert (e.g., an administrator) or user associated with a given client computing platform to interface withCDS system100A and/or external resources, and/or provide other functionality attributed herein to client computing platform(s). By way of non-limiting example, a given client computing platform may include one or more of a desktop computer, a laptop computer, a handheld computer, a tablet computer, a NetBook, a smart phone, a gaming console, a media console, a set top box, a kiosk, and the like.
• In one or more embodiments,CDS system100A includescomputing system102,PWD device130, andHCP device140, which are configured to communicate with each other by way ofcommunication network160.
• Computing system102 includesdata store104.Data store104 may be configured to store PWD data106 (or insulin therapy data).Data store104 includes one or more ofblood glucose data108, therapy settings110 (or insulin therapy settings), andinsulin dosing data112.PWD data106 may be received from external resources150 (e.g., therapy management system, a therapy management application, blood glucose sensors, insulin delivery devices, and combinations thereof). In one or more embodiments,blood glucose data108 may be raw blood glucose measurement, blood glucose estimates based on blood glucose measurements, and/or aggregations of the same, for example, trends or metrics.Blood glucose data108 can include date, time and value of one or more measurements of blood glucose.Blood glucose data108 may include timing information such as a time or time range associated with generating a blood glucose measurement from blood samples.
• In various embodiments,blood glucose data108 may be provided from any suitable glucose sensor. In some embodiments, a glucose sensor may be a continuous glucose monitor (CGM), a flash glucose monitor, a blood glucose meter (BGM), or any other suitable sensor. In the case of CGMs and flash glucose monitors, they may be configured to provide glucose data based on interstitial fluid glucose levels of a user, which may be correlated to blood glucose levels. A BGM may be configured to provide blood glucose data, typically based on a blood sample. Accordingly, the term “blood glucose” is not limited to using just blood glucose data, values, levels, etc., but is also intended to include interstitial fluid glucose levels, as well as any intermediate measurement values.
• Therapy settings110 may include information relevant to insulin for a PWD.Therapy settings110 may include, for example, brand names of long acting and rapid insulin, number and types of insulin delivery devices used by a PWD, typical dose amounts of insulin for certain carbohydrate intake events (e.g., low carb meal, medium carb meal, high carb meal), and typical dose amounts of insulin before physiological events such as exercise. In some embodiments,therapy settings110 may include individualized settings for a PWD, for example, a basal rate, a carbohydrate ratio, and/or an insulin sensitivity factor. For loop type delivery systems (e.g., artificial pancreas),therapy settings110 may include correction settings, that is, settings related to providing correction doses of insulin. Additionally,therapy settings110 may include correction information along with meal insulin for RA insulin delivery.
• Insulin dosing data112 may include dosing event information. Insulin dosing event information may include information about insulin dosing actions, for example a dosing time or time range, type of insulin (e.g., long acting (LA) insulin and rapid acting (RA) insulin), brand of insulin, and/or amount of dosed insulin. In some embodiments, dosing event information may include an indication of a dosing mechanism, for example, injection pen, inhaler, or infusion pump. Further, in some embodiments, dosing event information may include an indication of whether dosing event information, in part or in whole, is based on an actual dosing action (e.g., detecting insulin delivery, for example, based on a manual action of a pump or a control signal configured to cause insulin delivery), user tracking of dosing actions (e.g., a PWD or caregiver enters a dose using a therapy application executing on a mobile device), or inferred dosing actions (e.g., from capping/uncapping of an injection pen). Additionally, dosing event information may include removal of incorrectly inferred dosing actions (e.g., cap is removed and no dose was given).
• Processors120 may execute a number of engines for facilitating clinical decision support functions described herein. In one or more embodiments, such engines may includeinsights engine122,recommendation engine124,PWD engine126, andHCP engine128. More detailed description ofPWD engine126 andHCP engine128 are provided in further detail below.
• Insights engine122 may be configured, generally, to recognize and acknowledge patterns (“pattern” is used herein to mean both patterns and trends, and legal equivalents thereof) withinPWD data106, and such patterns may relate to, for example, system and user behavior. In various embodiments, insights may be classified as behavioral insights, insulin dosing insights and positive insights (for PWD). In one or more embodiments,insights engine122 may use rules and conditions to recognize an insight. Data, trends, and recommendations may be associated with insights for presentation to HCPs and/or PWDs.Recommendations engine124 may be configured, generally, to determine recommendations for HCPs responsive to insights generated byinsight engine122.
• CDS system100A, in various embodiments, includesexternal resources150.External resources150 may include sources of information outside ofCDS system100A, external entities participating withCDS system100A, and/or other resources. In some embodiments, some or all of the functionality attributed herein toexternal resources150 may be provided by resources included inCDS system100A.External resources150, in one embodiment, may include medical devices. Medical devices may include insulin delivery systems, including without limitation, insulin delivery devices (e.g., infusion pumps, injection pens, and inhalers), glucose sensors (e.g., CGMs and blood glucose meters), therapy managers (e.g., controllers for controlling open and closed-loop delivery of insulin or aspects of delivering insulin and recommendation systems for providing therapy recommendations to users and/or health providers), and combinations thereof.
• External resources150, in various embodiments, may include a therapy management system(s), an example of which will be described in further detail below. Therapy management systems may include, among other things, a diabetes management system for checking blood glucose data and therapy data and managing therapy settings.
• In some embodiments, one or more ofPWD engine126 andHCP engine128 may be located in a separate computing system (physically or logically) fromcomputing system102. In the example shown inFIG. 1B,HCP engine128 is located incomputing system103 for managing an HCP experience, andPWD engine126 is located in acomputing system102 for managing a PWD experience. Insights and recommendations (or indications thereof) generated byinsights engine122 and/orrecommendation engine124 may be sent toPWD engine126 andHCP engine128, which manage the user experience atPWD dashboard132 andHCP dashboard142, respectively. For example, summaries of insights and available recommendations may be sent toHCP engine128. The management (e.g., rules for) of how insights and recommendations are displayed and HCP input collected may be controlled byHCP engine128. Selected recommendations and insights, as well as any changes by the HCP (e.g., additional content or a new recommendation) may be sent tocomputing system102.Computing system102 may then send the selected recommendations and insights and any changes toPWD engine126, which manages the user experience for the PWD.
• FIG. 1B depicts an embodiment ofCDS system100B.CDS system100B is similar toCDS system100A, however,CDS system100B includescomputing system103 that is separate and distinct fromcomputing system102. In various embodiments,computing system102 andcomputing system103 operate, in whole or in part, in cloud computing, client-server, or other networked environment, or any combination thereof that are separate and distinct from another. Accordingly, atleast PWD engine126 operates in a computing environment that is separate and distinct from the computing environment associated withHCP engine128. In various embodiments described herein, features and functionality ofCDS system100A are the same or at least similar to the features and functionality ofCDS system100A. As such, reference to features/functionality ofCDS system100A, herein, may also reference features/functionality ofCDS system100B.
• CDS system100A (and/or100B) is configured, generally, to provide clinical decision support for HCPs in improving health outcomes for treating PWD. More specifically,insights engine122 is configured to analyzePWD data106, detect clinically relevant patterns, and detect therapy insights based on the detected patterns.Recommendations engine124 is configured, for each insight identified byinsights engine122, to determine if a therapy recommendation is warranted, and if so, identifies one or more therapy recommendations to associate with the insight.
• In one embodiment,insights engine122 may be configured to select one or more therapy insights, including those therapy insights listed in Table 1, Table 2, and Table 3 provided below. Table 1 includes behavioral insights, Table 2 includes insulin dosing insights and Table 3 includes positive insights. Behavioral insights, insulin dosing insights, and positive insights all being types of therapy insights, as more fully described herein. It should be appreciated that disclosed embodiments are not limited to the insights listed in the tables provided herein, which are non-limiting examples of insights, one or more of which, may be used by embodiments ofCDS system100A. In the example contemplated by Tables 1, 2 and 3, a PWD is using a CGM to capture blood glucose data.

• TABLE 1
  Examples of behavioral insights

  					Rele-
  			Recommendations		vant
  		Data/Trend Presented to	viewed by HCP		Set-
  Name	Rules/Logic to Trigger	HCP	(pushable to PWD)	Relevant Stat	tings	NPV Message
  Wear	For the most recent	Low sensor usage: For the	Consider wearing	% of time with	N/A	Want to trade some
  Sensor	14 days, when	past 14 days, patient wore	CGM more frequently.	an Active CGM		fingersticks for scans?
  More	a CGM is active for	sensor <85% of the time.	Consider reviewing	out of 14 days		CGM has benefits.
  	<85% of the total		this module on the			LINKS to LEARNING
  	available time, then		benefits of CGM			MODULES
  	trigger an insight.		Consider reviewing			View benefits of CGM
  	Reference:		this module on CGM			reusable learning
  	JDRF RCT published		troubleshooting			content
  	in NEJM					View CGM
  						troubleshooting
  						reusable learning
  						content
  Scan	For the time when	Too few scans: For	Consider scanning your	% of CGM data	N/A	A scan every
  More	a CGM is Active	the past 14 days, when	sensor at least every	captured out oftotal		8 hours won't
  	within the most recent	the patient was wearing	8 hours to capture	possible CGM data		hurt . . . but it can
  	14 days, if the total	a sensor, patient	as much CGM data as	available		connect the dots in
  	possible CGM	scanned too infrequently;	possible. It is especially	Avg number of		your glucose.
  	data points captured is	therefore, <90% of CGM	important to remember	scans/day		LINKS to
  	<90%, then trigger	data captured.	scanning before you go			LEARNING
  	an insight.		to bed and first thing in			MODULES
  			the morning, to capture			View benefits
  			your overnight glucose			of CGM reusable
  			levels. Consider			learning content
  			reviewing this module			View CGM
  			on the benefits of CGM			troubleshooting
  			Consider reviewing			reusable
  			this module on			learning content
  			CGM troubleshooting
  Check	For the most recent	Missed scan before RA	To get more informed	Avg number of	N/A	We missed an
  Glucose	14 days, count the	dose: Over the last	RA dose suggestions,	scans/day		opportunity to
  Before	number of RA doses	14 days, patient took	consider scanning or	Avg number of		support you . . .
  Dosing	when: No CGM scan	X number of RA doses,	checking BG before	fingersticks/day		scanning before
  	or BG was taken	and for X of those doses,	taking a RA dose	Avg number of RA		meals has benefits!
  	30 minutes prior	the patient did not scan	Consider reviewing this	doses/day		LEARNING
  	to the RA dose	sensor or check a BG	module on the benefits			MODULES
  		within 30-minutes	of CGM			View benefits
  		before taking the dose.	Consider reviewing this			of CGM reusable
  			module on correction			learning content
  			dose			View module
  						on correction
  						dose reusable
  						learning content
  Upload	The RA and/or	Gaps in connectivity	Consider syncing your	Last time RA cap		Consider syncing your
  Data	LA caps have	between the caps	LA and/or RA caps with	was synced to MA.		LA and/or RA caps
  More	not been synced in.	and the MA: Patient's	your mobile app more	Last time LA cap		with your mobile
  Fre-	the last 14 days	LA and/or RA caps	frequently. Syncing your	was synced to MA.		app more frequently.
  quently		have not been synced in	caps with the MA			Syncing your
  		14 days. Insights cannot	generates useful glucose			caps with the MA
  		be generated without	and behavioral insights.			generates useful
  		the necessary data.				glucose and
  						behavioral insights.
  Rebound	For the most recent 14	High after low: X times	Consider reviewing	Number of low	N/A	Pattern of high glucose
  High	days, count the number	patient had a high	treatment of lows with	events in the last		after a low glucose.
  	of highs (>180 mg/dL)	glucose event after	rule of 15.	14 days. A low		LEARNING
  	[BGM or CGM value]	a low glucose event.	Consider reviewing this	event begins when		MODULES
  	that: Occur within	These events exclude	module on possible	glucose falls below		View module on
  	<3 hours of a preceding	periods of time when an	causes of low glucose,	70 mg/dl and ends		possible causes of low
  	low (<70 mg/dL)	RA dose was given after	detection and	when glucose rises		glucose, detection and
  	RA dose not given	lows.	management	above 80 mg/dl.		management reusable
  	Note: Trigger if the					learning content
  	fraction of lows
  	followed by rebound
  	highs is greater than 0.25.
  Dose for	For the most recent	Highs likely caused by	Consider having a	Avg number		Pattern of missed
  Every	14 days, count the	missed meal doses: X times	reminder to take your	of RA doses/day		meal doses.
  Meal	number of missed RA	patient had a high glucose	meal insulin for food	(over the last		LEARNING
  	doses using a missed	event likely caused by a	Contact me if you are	14 days)		MODULE
  	dose detection algorithm,	missed meal dose. This was	still experiencing high			View module on
  	Calculate the probability	observed through a high	glucose			possible causes
  	of missing a dose as	glucose rate of change	Consider reviewing this			of high glucose,
  	number of missed doses/	with no RA dose.	module on possible			detection and
  	(number of doses +		causes of high glucose,			management reusable
  	number of missed doses).		detection and			learning content
  	Trigger insight if the		management
  	probability of a missed
  	dose is larger than 0.01.
  Dose	Count fraction of	Large time gap between	Consider taking your			Consider taking
  Before	RA doses when:	eating and taking a rapid	rapid acting dose about			your rapid acting dose
  Eating	Rate of change at the	acting dose: for x % of all	15 minutes before you			about 15 minutes
  	time of RA dose is	RA doses, the rate of	begin eating. This will			before you begin
  	above 2 mg/dl/min	change when taking	reduce your after meal			eating. This will
  	No previous RA dose	the dose was above	peak glucose and			reduce your after
  	was taken in the last 2	2 mg/dl/min, suggesting	improve your overall			meal peak glucose
  	hours	that the PWD started	time in range.			and improve your
  	Glucose did not	eating before taking				overall time in range.
  	go below 70 mg/dl	their injection.				LEARNING
  	in the last 3 hours					MODULE
  	Maximum glucose					View module on
  	0-4 hours after the RA					possible causes of high
  	dose was above					glucose, detection and
  	180 mg/dl					management reusable
  	Trigger if fraction of					learning content
  	doses labeled as delayed
  	is greater than 0.25.
  Take LA	For the most recent	High caused by missed	Consider having a			It's agood habit
  Dose
  	14 days, count the	long acting doses: X LA	reminder to take your			to take your LA doses
  Every	number of days when:	doses were missed or	long acting insulin dose			within 24 hours of
  Day	The time between	taken too late in the				each other. Consider
  	two consecutive long	past 2 weeks; the				having a reminder to
  	acting does is more	patient had a high				take your long
  	than 24 hours	glucose event caused				acting insulin dose
  	Glucose rises above	by a missed LA dose.				LEARNING
  	150 mg/dL (BGM or					MODULE
  	CGM value) in the					View module on
  	time period 24 hours					possible causes of high
  	after the last LA dose					glucose, detection and
  	until the next LA dose					management reusable
  	and more than 3 hours					learning content
  	after an RA dose
  	Glucose rises above
  	250 mg/dL (BGM or
  	CGM value) in the
  	time period 24 hours
  	after the last LA dose
  	until the next LA dose
  	and within 3 hours of
  	an RA dose
  	Trigger if the number
  	of missed
  	LA doses in the last 2
  	weeks is greater than 3.

• TABLE 2
  Examples of insulin dosing insights

  				Rele-	Rele-
  			Recommendations	vant	vant
  	Rules/Logic	Data/Trend	viewed by HCP	Statis-	Set-
  Name	to Trigger	Presented to HCP	(pushable to PWD)	tics	tings	NPV Message
  Needed	For the most recent	Need more insulin for	If fraction of times more	See	RA	You have either
  more	14 days, count the	meals: For days when a	insulin was needed	results/	Dose	gone high or taken
  insulin	number of times:	breakfast/lunch/dinner RA	is greater than 0.75:	logic	setting	a correction dose
  for	the lowest glucose	dose was given, more insulin	“Consider increasing	trigger		after your breakfast/
  meals	level 2-4 hours after	was needed X out of Y times	breakfast/lunch/dinner			lunch/dinner dose
  	the meal dose was	(e.g. 5 out of 14 times).	RA dose by X units			X out of Y times.
  	above 150 mg/dl, OR	Glucose was high after the	(10% rounded to 1			LEARNING
  	the lowest glucose	meal N out of M times and a	unit).”			MODULES
  	level 2-4 hours after	correction dose was given O	If fraction of times more			View module on
  	the meal dose was	out of P times.	insulin was needed			possible
  	above 70 mg/dl, and		is 0.25-0.75 times:			causes of highs
  	a correction dose		Consider reviewing this			View module on
  	was given after the		learning model on			corrections (reusable
  	meal dose AND		estimating meal size and			learning content)
  	Glucose levels did not		adjusting dose based on
  	fall below 70 mg/dl 0-4		meal size.
  	hours after the meal
  	dose
  	Only trigger if:
  	the “need less insulin”
  	after the same meal
  	insight is not triggered
  	the fraction of doses
  	needing more insulin
  	was greater than 0.25
  	The “need to increase
  	LA dose” insight is not
  	triggered.
  	Whether an RA dose
  	corresponds to a small,
  	medium, or large meal is
  	determined by:
  	Calculating
  	recommended
  	correction dose at the
  	time of meals based on
  	PWD settings and
  	glucose at the time of
  	meal
  	Administered meal
  	dose = administered
  	dose − recommended
  	correction dose
  	Calculate
  	breakfast/lunch/dinner
  	meal dose
  	recommendations
  	based on PWD settings
  	Choose the category
  	where the absolute
  	difference between the
  	administered meal dose
  	and the category
  	dose is smallest
  	A correction dose
  	is a dose closer is
  	size to the estimated
  	correction dose
  	than to the estimated
  	correction
  	dose + small meal
  	dose recommendation.
  Needed	For the most recent	Need less insulin for meals:	If fraction of times less	See	RA	You have gone
  less insulin	14 days, count the	For days when a	insulin was needed is	results/	dose	low X out of Y
  for meals	number of times:	breakfast/lunch/dinner RA	above 0.25: Consider	logic	setting	times following a
  	the lowest glucose	dose was given, less insulin	reducing your meal dose	trigger		breakfast/lunch/
  	level 45 min-5 hours	was needed X out of Y times	X units (10% rounded to			dinner RA dose.
  	after the meal dose was	(e.g. 5 out of 14 times).	by 1 unit).			LEARNING
  	below 70 mg/dl, AND		Consider			MODULES
  	no correction dose was		reducing your meal			View module on
  	given after the meal		dose after physical			possible causes of
  	AND		activity or in preparation			lows
  	The “need to lower		of physical activity			View module on
  	LA dose” insight is not		in the next 2 hours.			corrections (reusable
  	triggered		Alternatively, consider			learning content)
  	Only trigger if fraction of		eating a snack before
  	doses with low after the		physical activity.
  	meal is greater than 0.25.
  Need	For the most recent	Need more insulin for	Consider increasing	See	Cor-	You have
  more	14 days, count the	corrections: For days when a	your morning/	results/	rection	remained high after
  insulin for	fraction of RA doses	RA dose with correction was	afternoon/evening	logic	dose	correction doses
  corrections	given when: the lowest	given, more insulin was	correction doses by X	trigger	settings	X out of Y times.
  	glucose level 2-4	needed X out of Y times.	(10% rounded to			LEARNING
  	hours after a dose was		the nearest 1 unit)”.			MODULES
  	above 150 mg/dl,					View
  	AND					module on possible
  	glucose levels
  	did not fall below					causes ofhighs
  	70 mg/dl 0-4 hours					View module on
  	after the correction					corrections (reusable
  	dose AND					learning content)
  	no additional
  	meal RA was given
  	0-4 hours after the
  	initial dose
  	(A meal
  	RA is defined as an
  	RA where the dose
  	administered
  	was closer to the
  	recommended
  	small meal dose +
  	recommended
  	correction dose than
  	to the recommended
  	correction dose.)
  	Only triggered if:
  	number
  	of RA doses given
  	when glucose values
  	were above target
  	exceeded 5
  	fraction of
  	correction doses where
  	more insulin was
  	needed exceeds 0.5
  	“needed more insulin
  	after bf/lunch/dinner”
  	insight is not triggered
  	“Need more LA
  	insulin” insight is not
  	triggered
  	“need less insulin after
  	correction” in the same
  	time period is not
  	triggered
  Need less	For the most recent	Need less insulin for	Consider decreasing	See	Cor-	You have gone low
  insulin for	14 days, count the	corrections: For days when a	your	results/	rection	after correction doses
  corrections	fraction of RA doses	RA dose with correction was	morning/afternoon/	logic	dose	X out of Y times.
  	given when glucose	given in the	evening correction	trigger	settings	LEARNING
  	values were above	morning/afternoon/evening,	doses by X (10%			MODULES
  	target followed by:	less insulin was needed X out	rounded to			View module
  	Glucose levels falling	of Y times.	the nearest 1 unit)			on possible
  	below 70 mg/dl					causes of lows
  	45 min-5 hours					View module on
  	after correction dose					corrections (reusable
  	AND					learning content)
  	no additional
  	correction RA was
  	given 0-4 hours after
  	the initial dose
  	Only triggered if:
  	number
  	of RA doses given
  	when glucose values
  	were above target
  	exceeded 5
  	fraction
  	of correction doses
  	where less insulin
  	was needed
  	exceeds 0.25
  	“needed
  	less insulin after bf/
  	lunch/dinner” insight is
  	not triggered
  	The “need
  	to decrease LA dose”
  	insight is not triggered
  Need to	For the most recent	Low During Sleeping-	Consider reducing	X LA	LA	Pattern of lows
  decrease	14 days, count the	Hours: Patient experienced	LA insulin by	Doses	Dose	detected overnight/
  LA dose	number of days	lows during sleeping hours on	X units (10% rounded	Given/	Settings	between meals
  	when one or more lows:	X out of Y nights when an LA	to the nearest unit)	14 days
  	Within 24 hours of	dose was given.	Consider reviewing this			LEARNING
  	a LA dose		module on possible			MODULES
  	Overnight		causes of low glucose,			View module on
  	(where overnight is		detection and			possible causes
  	defined as the longest		management.			of low glucose,
  	period of time between					detection and
  	2 RA doses in					management reusable
  	a 24-hour period)					learning content
  	More than 4 hours after
  	the last RA dose
  	Note:
  	Trigger if:
  	Fraction of nights
  	with low
  	overnight
  	attributed to LA
  	dose exceeds 0.25
  	Need more
  	insulin for meals
  	insight is
  	triggered
  	In this context, a low is
  	defined as a BGM
  	value below 70 mg/dl
  	or three consecutive
  	CGM values below 70
  	mg/dl.
  Need to	For the most recent	Glucose rising during	Consider increase	X LA	LA	Pattern ofhigh
  increase
  	14 days, count the	sleeping hours: For days	in long acting insulin by	Doses	Dose	glucose overnight
  LA dose	fraction of days when:	when a LA dose was given,	X units (10% rounded	Given/	Settings	LEARNING
  	Glucose values are	patient experienced X high	to the nearest unit)	14 days		MODULE
  	increasing overnight	glucose events during	Consider reviewing this			View module on
  	Overnight is	sleeping hours.	module on possible			possible causes
  	defined by		causes of high glucose,			of high glucose,
  	identifying the		detection and			detection and
  	longest period of		management			management reusable
  	time between 2 RA		Consider reviewing			learning content
  	doses in a 24-hour		material on snacks at
  	period), and		bedtime and what
  	considering the		relationship stress and
  	period oftime 3		illness have on high
  	hours after the first		glucose
  	RA dose until the
  	second RA dose
  	Increasing is
  	defined as glucose
  	at the beginning of
  	the time range
  	being at least 30
  	mg/dl lower than
  	glucose before the
  	second RA dose
  	Glucose never drops
  	<100 mg/dL
  	Maximum overnight
  	glucose >130 mg/dl
  	Note:
  	Trigger only if fraction
  	of overnight highs
  	attributable to LA dose
  	exceeds 0.6.

  
  
• For each behavioral insight and insulin dosing insight thatinsights engine122 is configured to detect,insight engine122 and/orrecommendation engine124 store an insight name, triggering logic, a template for explaining the data/trend that triggered the insight (and presentable to the HCP), a list of available behavioral recommendation(s), and a template for displaying relevant statistics(s). These fields correspond to columns headings for Table 1 and Table 2. For each positive insight thatinsights engine122 is configured to detect,insights engine122 stores an insight name, and rules that trigger it. These fields correspond to column headings for Table 3.
• Behavioral insights are detectable byinsights engine122 from patterns in thePWD data106. The patterns are pre-associated with behavioral insights. For example,insights engine122 is configured to identify one or more of eight behavioral insights (i.e., detect patterns in thePWD data106 associated with the behavioral insights) shown in Table 1: (1) wear sensor more; (2) scan CGM more, (3) check glucose before dosing; (4) upload data more frequently; (5) rebound high; (6) dose for every meal; (7) dose before eating; and (8) take a long acting dose every day.
• Additionally or alternatively, insulin dosing insights are also detectable byinsights engine122 from patterns in thePWD data106. The patterns are pre-associated with insulin dosing insights. For example,insights engine122 is configured to identify one or more of six insulin dosing insights (i.e., detect patterns in thePWD data106 associated with the behavioral insights) shown in Table 2: (1) needed more insulin for meals; (2) needed less insulin for meals; (3) need more insulin for corrections; (4) need less insulin for corrections; (5) need to decrease LA dose; and (6) need to increase LA dose.
• Additionally or alternatively, positive insights are detectable byinsights engine122 from patterns in thePWD data106. The patterns are pre-associated with positive insights. For example, insights engine is configured to identify one or more of twelve positive insights (i.e., detect patterns in thePWD data106 associated with the behavioral insights) shown in Table 3: (1) best time in range day in the last month; (2) best time in range week in the last 3 months; (3) good glucose streak, in days; (4) good glucose streak, in weeks; (5) wearing your sensor more in the past 2 weeks; (6) scanning your sensor more in the past 2 weeks; (7) checking glucose before RA dose in the past 3 months; (8) thanks for uploading data; (9) good job dosing before meals; (10) remembering to take LA dose; (11) no rebound highs; and (12) changed RA or LA dose.
• It should be appreciated thatinsights engine122, in various embodiments, may comprise multiple insights engines. In one embodiment,insights engine122 may include a behavioral insights engine, insulin dosing engine, and a positive insights engine. These insight engines may operate independently of each other, in other words, they may operate as independent processes withinCDS system100A, identifying insights.
• FIG. 8 illustrates a method800 for automated provisioning of clinical advice to a provider caring for a PWD. Method800, in various, embodiments, may be implemented byCDS system100A.
• At810 of method800, a clinically relevant pattern in therapy data of a PWD under the care of a provider is detected. For example,insights engine122 receives PWD data106 (e.g., therapy data) fromdata store104. Upon receivingPWD data106,insights engine122 detects a clinically relevant pattern in thePWD data106.
• A clinically relevant pattern can be any pattern of data that is relevant to the therapy of a PWD. A clinically relevant pattern, in various embodiments, is a detected pattern in thePWD data106 associated with the behavioral insights. For example, a clinically relevant pattern is a pattern listed in the “Relevant Statistics” column of Tables 1 and 2 (e.g., Table 1,Row 1, Column 5: % of time with an active CGM out of 14 days).
• At820 of method800, a predefined behavior of a PWD is identified responsive to the detected clinically relevant pattern. For example,insights engine122 identifies a predefined behavior of a PWD. A predefined behavior, in various embodiments, is a behavior described in “Data/Trend Presented to HCP” Column of Tables 1 and 2 (e.g., Table 1,Row 1, Column 3: Low Sensor Usage: For the past 14 days patient wore sensor <85% of time).
• In various embodiments, the clinically relevant detected pattern is indicative of desirable behaviors and/or undesirable behaviors. An example of a detected pattern indicative of an undesirable behavior is the “low sensor usage” pattern (Table 1,row 1, column 3). The “low senor usage” pattern is indicative of an undesirable behavior because low usage of a CGM by a PWD does not enable the CGM provide a complete set ofPWD data106 for use by computingsystem102.
• An example of a detected pattern indicative of a desirable behavior are patterns in Table 3. In various embodiments, the patterns in the rules/logic to trigger column are indicative a desirable behaviors, such as the “time in range in the last day was better than in any other day in the last month” pattern associated with the “best time in range day in the last month” insight. The “time in range in the last day was better than in any other day in the last month” pattern is indicative of a desirable behavior because the increased time in range for a PWD improves health outcomes for the PWD.
• At830 of method800, a therapy insight associated with the identified predefined behavior is selected. For example,insights engine122 selects a “wear sensor more” insight (Table 1,row 1, column 1) associated with a “low sensor usage” pattern (Table 1,row 1, column 3).
• In various embodiments, a therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation. Examples of behavior recommendations are provided in the “recommendations viewed by an HCP” column (e.g., Column 4) of Tables 1 and 2. In one example, the behavior recommendations for a “wear sensor more” insight includes (1) consider wearing a CGM more frequently, (2) consider reviewing a module on the benefits of CGM, and (3) consider reviewing a module on CGM troubleshooting.
• At840 of method800, responsive to the selecting the therapy insight, the selected therapy insight automatically sent to a provider-dashboard associated with the provider caring for the PWD. For example,HCP engine128 automatically transmits an insight (e.g., “wear sensor more” insight) toHCP device140 in response to selection of the insight. UponHCP device140 receiving an insight, the insight is presented (e.g., displayed) onHCP dashboard142.
• In various embodiments, more than one insight is sent to theHCP device140 where each insight includes at least one recommendation. For example,HCP engine128 sends three separate insights toHCP device140. Each of the insights includes an associated recommendation. As such, HCP engine129 sends at least three separate recommendations toHCP device140.
• In one embodiment, a therapy insight, once created, includes clinical advice that includes one or more behavior recommendations. For example, a “wear sensor more” insight (in the first row of Table 1) includes behavior recommendations (listed incolumn 4 of Table 1). The behavior recommendations for a “not wearing sensor” insight includes the following recommendations: (1) consider wearing a CGM more frequently, (2) consider reviewing a module on the benefits of CGM, and (3) consider reviewing a module on CGM troubleshooting. The “scan more” insight (in the second row of Table 1) includes behavior recommendations (listed incolumn 4 of Table 1). The behavior recommendations for a “scan more” insight includes, among other things consider scanning your sensor at least every 8 hours to capture as much CGM data as possible.
• In various embodiments, therapy insights may be positive or negative insights. A negative insight, for example, may correspond to patterns of behavior or insulin dosing that are correlated to decreased health benefits, or increased health risks or complications. For example, a “wear sensor more” insight (in the first row of Table 1) may be characterized as a negative insight. In such an example, a negative insight is based, at least in part on, therapy data that a PWD does not wear their sensor regularly, which is triggered when over a previous 14 day period a CGM was active less than 85% of the time.
• A positive insight, in some embodiments, correspond to patterns of behavior that are correlated to increased health benefits or reduced health risks or complications. For example, a “best time in range day in the last month” insight (in the first row of Table 3) may be characterized as a positive insight. In such an example, a positive insight is based, at least, in part on, therapy data that a glucose level of a PWD is in range, for a time period, in the last day that was better than in any other day of the last month.
• As described herein,insights engine122 selects insights based, at least in part, onPWD data106. In various embodiments,PWD data106 used byinsights engine122 is current PWD data within a predetermined time frame (e.g., data from the most recent two weeks).
• In various embodiments, therapy insights (that are selected by insights engine122) are provided toHCP engine128, which manages the HCP user experience at theHCP dashboard142. For example, one or more insights (e.g., “not wearing sensor” insight) detected byinsights engine122 may be provided toHCP engine128, which then provides therapy insights to theHCP dashboard142, and then one or more therapy insights may be presented to an HCP on HCP dashboard.
• In various embodiments,HCP engine128 may be configured, generally, to generate system statistics; manage, send, and/or present therapy insights to an HCP; and receive HCP feedback, including without limitation, feedback about therapy insights presented at theHCP dashboard142.
• In some embodiments,HCP engine128 may be configured to assign and re-assign priorities to therapy insights, which priorities may effect an order according to which therapy insights are presented (e.g. displayed) atHCP dashboard142. By way of example, therapy insights corresponding to patterns of behavior that are more frequently recognized for a PWD may be presented above (e.g., in rank order) less frequently recognized insights. By way of another example, insights that are correlated with serious health risks, such as risk of hypoglycemia or risk of DKA, may be presented above insights less correlated with serious health risks. In one embodiment, therapy insights may be prioritized based, at least in part, on the impact to the PWD. An example prioritization, based on impact, may be LA insulin change, RA insulin change, behavioral improvement, and kudos for good behavior. In such an example, LA insulin change has a greater therapy impact to the PWD than a RA insulin change. Likewise, an RA insulin change has greater therapy impact to the PWD than a behavioral improvement and so on. It should be appreciated that prioritization of the insights may be customizable, for example, by a PWD and/or HCP.
• The therapy insight provided to an HCP support an HCP in improving outcomes for a PWD under the care of an HCP. Examples of improved outcomes include, but are not limited to, increased time within a target glucose range, reduced episodes of severe hypoglycemia, and reduced episodes of hyperglycemia. Further, as a result of such outcomes, a PWD's risk of long-term diabetic related complications is reduced. More specifically, if a PWD follows the recommendations that are provided by the system, that behavior is correlated to the improved health outcomes. So, improved outcomes can be attributed at least in part to information presented to an HCP on anHCP dashboard142 as described with respect toFIGS. 2A-F.
• FIG. 2A depicts an example triagedpatient list200 presentable toHCP dashboard142, in accordance to various embodiments. Anexample patient204, “Mark Jackson” is the first patient listed inpatient list200. It is noted that the patient list may be triaged based, at least in part, on insights (e.g., the number of insights associated with a patient).Flag206 is presented and is an indicator thatpatient204 may need attention. Also shown is some summary information about Mark Johnson's therapy, including estimated A1c (a picture of average blood glucose control for a given period of time), percentage of glucose measurements within (or outside) a target range, here, between 70 mg/dL and 180, a number of active insights.
• Patient list200 includescolumn201 that indicates the number of active insights associated with a particular patient. As depicted inFIG. 2A, Mark Jackson has two active insights (as listed in column201). For example, the two active insights are the wear sensor more insight (Table 1, row 1) and the scan more insight (Table 1, row 2).
• In one embodiment, any number of boxes208 (associated with a patient name) may be selected. Upon selection, a batch report of the selected patients is created. For example, a batch report may be printed.
• In various embodiments, a patient inpatient list200 is selectable. For example, upon selection of a patient inpatient list200, summary view (or overview205) may be presented atHCP dashboard142, as shown inFIG. 2B. In various embodiments, a patient is selectable inpatient list200 by, hovering over the patient's name and selecting the patient in the list.
• FIG. 2B depicts an embodiment of anoverview205 of an insight presented to an HCP onHCP dashboard142. In one embodiment, an insight inoverview205 is associated with a PWD (e.g., Mark). It should be appreciated thatoverview205 can include a list of insights (e.g., at least two insights) presented to an HCP onHCP dashboard142 for an HCP.Overview205 includes various sections associated with various components of an insight. For example,overview205 includes the following sections:glucose data210, statistics andinsights220,settings230 andrecommendation history240. It should be appreciated that the order of sections210-240 displayed onHCP dashboard142 may be adjusted.
• FIG. 2C depicts an embodiment of glucose data210 (or glucose data section) ofoverview205 ofFIG. 2B.Glucose data210 includes anexample plot211 of various glucose distributions over time. In one embodiment,plot211 of glucose data (e.g., interstitial glucose data) is based at least in part onPWD data106, such as glucose data. On the left side ofplot211, Y-axis212 shows values for Mark Johnson's blood glucose levels over time in milligrams per deciliter (mg/dL), and such glucose levels are shown according to a linear scale, here, 50 mg/dL changes. On the right side ofplot211,X-axis213 shows frequency distribution ranges over time. In this example, a 10/90 interquartile range (IQR) of blood glucose values is defined by upper dashedline214A and lower dashedline214B. A 25/75 IQR of blood glucose values is defined by uppersolid line216A and lowersolid line216B. A median for blood glucose values is shown bysolid line217. A target range for blood glucose values is defined between upper bound218 (here 180 mg/dL) and lower bound219 (here 70 mg/dL).
• X-axis213 shows times in one hour increments over a 24-hour time period. In one or more embodiments, the increments and time period may be settings and may be increased or decreased as needed. Additionally,plot211 overlays glucose data of various days over a 24-hour time period
• FIG. 2D depicts an embodiment of statistics andinsights220. In various embodiments, statistics and insights presented inoverview205 are generated byinsights engine122. Statistics andinsights220 includes three sections,system statistics221,system insights222, andsystem recommendations223.System stats221 may include data captures, CGM usage in days out of a time period, long acting doses during a time period, and number of rapid acting doses per day. For example,system stats221 shows that 95% of data was captured, CGM was used 10 out of 14 days, 10 long acting does were detected over the 14 day period, and two rapid acting doses were given per day.
• System insights222 may include one or more insights for a patient, in accordance with one or more embodiments of the disclosure. Here,system insights222 includes twoprimary insight regions224 and228, defined in part by a separator.Primary insight region224 includesprimary insight225, here, “wear sensor more.”Primary insight region224 also includes detectedbehavior statement226, here “pattern of not wearing your CGM between XX:XXPM-YY:YYPM.” An associated time period during which the behavior was detected is provide with detectedbehavior statement226, and, in some embodiments, such time period may be the same as the time period defined onX-axis213 ofplot211 or be within the time period defined onX-axis213 ofplot211. Providing a time period withstatement226 gives a user a convenient guidepost to associate at least part ofplot211 withstatement226. An HCP may compare data shown at theplot211 with detectedbehavior statement226 using the time period and, for example, decide if she agrees.
• Primary insights region224 also includesexplanation227, which includes one or more explanation messages about importance or implications ofprimary insight225. Here,explanation227 includes two explanation messages: first, “outcomes may be improved if the CGM is worn for a longer period of time;” and the second “correlates to using the system at least 6/7 days per week as correlated with improved outcomes by JDRF RCT.”
• Similar toinsight region224,insight region228 includes an insight statement (e.g., scan more), detected behavior statement (e.g., too few scans) and explanation (e.g., For the past 14 days, when the patient was wearing a sensor, patient scanned too infrequently; therefore, <90% of CGM data captured).
• In various embodiments, any number of insights of Tables 1-3 can be presented ininsights222. For example,insights222 can include any number of insights from Table 1, any number of insights from Table 2 and/or any number of insights from Table 3.
• System recommendations223 includes arecommendation list250. In various embodiments, recommendations presented inoverview205 are generated byrecommendation engine124.Recommendation list250 is presented to an HCP for review, editing, and/or approval. Upon approval,recommendation list250 is provided to a PWD, for example, sent toPWD device130 for display atPWD dashboard132.
• Recommendation list250 includesrecommendations251,252 and253, the textual contents of which were generated by a clinical decision support system, such asCDS system100A ofFIG. 1A. In the example shown inFIG. 2D,recommendation251 is “consider wearing CGM,”recommendation252 is “Consider viewing learning module on benefits of CGM reusable content” andrecommendation253 is “Consider view learning module on CGM troubleshooting reusable learning content.”
• In various embodiments, recommendations251-253 are individually selectable (for review, editing and/or approval by an HCP), as indicated by the boxes surrounding each recommendation. For example, an HCP may select each of recommendations251-253 to be sent to a PWD. In another example, an HCP may select a recommendation (e.g., recommendation253) to be removed fromrecommendation list250 such that the recommendation is not sent to a PWD. In a further example, an HCP may add a recommendation torecommendation list250.
• FIG. 2E depictssettings230 ofoverview205, in accordance with embodiments.Settings230 may be configured, generally, to display therapy settings for a patient. In the example shown inFIG. 2E, therapy settings for long acting doses, rapid acting dose with correction, and rapid acting dose without correction are shown. Also shown is historical dosing information for each of the foregoing. The historical dosing information may indicate if there is a change to the therapy settings in response to an insight and what that change would be so an HCP can see the delta before insights are sent to a PWD. Additionally, historical dosing information may include initial first time or ongoing therapy settings changes that are independent from setting changes based on insights. In various embodiments,settings230 may depict that the settings are updated by a system (e.g., loop system or auto-titration), by an HCP, or by a PWD.
• FIC.2F depictsrecommendation history240 ofoverview205, in accordance various embodiments.Recommendation history240 is a log of therapy suggestions approved to send to a patient. In the example shown inFIG. 2F,recommendation history240 includes a number of entries, and for each entry may include a date sent, a summary or synopsis of recommendations and/or care notes sent that date, and a patient status, which is an indication of whether a patient accepted the recommendation and/or care notes (i.e., received and viewed it). The patient status may initially be set to an intermediate status, for example, “waiting” or “unviewed.” Additionally or alternatively, a patient status may be set as “pending” if a PWD has not seen a recommendation/care notes (e.g., there is no indication that a recommendation/care note has been presented at a PWD's device or dashboard) or “snoozed” a viewing of recommendations/care notes. In one embodiment, while a patient status is “pending,” an HCP can update or revise the pending recommendation. In various embodiments, when a patient views the related therapy suggestion atPWD dashboard132, a “read” or “reviewed” message is sent, for example, toPWD engine126 and/orHCP engine128 and an update sent toHCP dashboard142 to change patient status for an associated recommendation and/or care note for recommendation history.
• In one embodiment, care notes are sent to a PWD. However, in various embodiments, care notes are not sent to a PWD.
• FIG. 3 depicts anexample workflow300 for selecting and sending recommendations to a PWD viaHCP dashboard142, in accordance with one or more embodiments of the disclosure. In theexample workflow300 shown inFIG. 3, adialogue box302 is displayed responsive to selection of one or more recommendations to send. For example,dialogue box302 is displayed in response to an HCP selecting a recommendation (e.g., recommendation251) in a list of recommendations (e.g., recommendations list250) displayed onHCP dashboard142.
• Dialogue box302 may include asummary304 for each insight for which a recommendation message was selected. In this example,summary304 includes insight description, a pattern description, and a proposed recommendation message.Dialogue box302 also includes atext box306 that may be used to enter notes, messages, or attach documents. For example, in response selecting a recommendation corresponding with an insight associated with a high glucose level, an HCP may enter information or messages for a PWD associated with the recommendation.
• In some embodiments, content for a medically relevant note may be entered attext box306. For example, the content may be part of an SOAP note (subjective, objective, assessment and plan note) that documents an aspect of a PWDs therapy.
• Upon selection ofbutton308, “confirm” a confirmationrequest dialogue box310 is displayed, which queries a user to confirm that the recommendations and/or care notes should be sent to the patient. In some embodiments, there may be an option to add an additional care notes (and/or revise care note in text box306). Selection ofbutton312, “send” causes the recommendation to be sent to a PWD and forconfirmation dialogue box314 to be displayed with the message “recommendation sent.” In various embodiments, upon selection ofbutton312, the recommendation is sent to the PWD and/or content of a care note may be automatically saved to a care note of an electronic medical records (EMR).
• FIG. 4 depicts anexample view400 for displaying various information associated with recommendations, therapy changes and/or care notes usingHCP dashboard142, in accordance with one or more embodiments of the disclosure. View400 includes various sections, such as,introduction410, selectedrecommendations420, recommended settingchanges430 and care notes440.Introduction410 allows an HCP to enter a personalized introduction (or message) to a PWD regarding selectedrecommendations420 and/or recommendation setting changes430. It is noted thatintroduction410 is separate and distinct from care notes440. For example,introduction410 may include a personalized introduction (or overview) to the insights/recommendations, while care notes440 include medically relevant information specifically associated with insights/recommendations. For example, an introduction may include the following message to the PWD: “Hi Mark, I have noticed a pattern of high glucose and have increased your meal time insulin. Please review and accept these updates. Sincerely, Dr. Eliana” (seeFIG. 7C). In various embodiments,introduction410 may be a preset message that is auto-compiled based, at least in part, on one or more insights/recommendations sent to a PWD. Alternatively,introduction410 may be created by an HCP.
• Selected recommendations420 are one or more recommendations selected by an HCP to be sent to a PWD. As depicted inFIG. 4, selectedrecommendations420 includes recommendation421 (e.g., Consider wearing CGM recommendation), recommendation422 (e.g., Consider reducing meal insulin by 20%), recommendation423 (e.g., Consider reducing correction insulin by 20%), and recommendation424 (e.g., Consider increasing long acting insulin by 10%). In various embodiments, recommendations may indicate percentage increase or decrease of insulin dosage, amount of units increase or decrease of insulin dosage, or simply directional increase/decrease of insulin therapy setting changes.
• The list of recommendations may correspond to one or more insights (e.g., insights in Table 1, 2 and/or 3).
• Recommended setting changes430 (or adjusted insulin therapy settings) are one or more recommended therapy setting changes that may correspond to one or more of selectedrecommendations420. Recommended setting changes430 includes recommended settingchange431 to change a current dose (e.g., a large meal dose of insulin of 5 units) to an adjusted dose (e.g., 4 units), recommended settingchange432 to change a current dose (e.g., correction dose for 351-400 mg/dL of 5 units) to an adjusted dose (e.g., 4 units), and recommended settingchange433 to change a current dose (e.g., correction does for over 400 mg/DL of 6 units) to an adjusted dose (e.g., 5 units). In one embodiment, one or more of the recommended setting changes are accepted. As a result, the therapy settings are adjusted to the accepted recommended setting changes.
• Care notes440, in one embodiment, are similar to care notes entered into text box306 (FIG. 3). Content of care notes440 may comprise information and other documentation added, for example, by an HCP. Upon selection ofsend button442, the information inintroduction410, selectedrecommendations420, and recommended settingchanges430 are sent to a PWD. Content incare note440 may also be sent to a PWD. Alternatively or in addition, in one embodiment, content of care notes440, selectedrecommendations420, and recommended settingchanges430 may be saved to an EMR system. In one embodiment, the content may be saved in fields for an SOAP note, or saved and referenced by an SOAP note reporting feature of an EMR system.
• FIG. 5 depicts aview500 of an insight displayed onHCP dashboard142.
• View500 includes insight510 (e.g., need more insulin for meals) and recommendation512 (e.g., consider increasing rapid-acting meal doses) associated withinsight510. An HCP may approve or select the insight/recommendation to be presented to a PWD by selectingselect button514.
• View500 includesinsight button516, that when selected,view500 is displayed. View500 includesstatistics button518, that when selected, displays a statistics view associated withinsight510. View500 includesinformation button520, that when selected, displays information associated withinsight510. In various embodiments, the information displayed in response to selection ofinformation button520, may include, but is not limited to, information in any one of tables 1-3 (e.g., relevant stat, data/trend information).
• FIG. 6 depicts aview600 of an insight displayed onHCP dashboard142.
• View600 includes insight612 (e.g., remembering to take long-acting dose) and recommendation614 (e.g., encourage Mark to keep up the good work) associated withinsight612. An HCP may approve or select the insight/recommendation to be presented to a PWD by selectingselect button514.
• View600 includesinsight button618, that when selected, displays view600. View600 includesstatistics button620, that when selected, displays a statistics view (e.g., statistics view660) associated withinsight612.
• FIG. 6 also depictsview660, which is a statistics view ofinsight612, whenstatistics button620 is selected. View660 includesrelevant statistics662 associated withinsight612. For example, relevant statistics includes time inrange664, time in high665 and time in low668. It should be appreciated that relevant statistics can be any statistic associated with insight612 (e.g., blood glucose level, time of active sensor, number of missed doses, etc.)
• Time inrange664 depicts a 15% increase in the time in range in the past 14 days (e.g., from 50% to 65%). Time in high665 depicts a 15% decrease of the duration of time in a high blood glucose range in the last 14 days (e.g., from 47% to 32%). Time in low668 depicts a 0% change of the duration of time in a low blood glucose range in the last 14 days (e.g., no change from 3%).
• FIG. 7A illustrates anexample summary view700A presented atPWD dashboard132.Summary view700A is configured, generally, to show behavior recommendations, therapy settings recommendations, and contextual information.Summary view700A, in various embodiments, is displayed by selecting a request to look at updated information since receiving recommendations from an HCP, in accordance with one or more embodiments of the disclosure. For example, a PWD selects “learn more” button711 (in view710) ofsummary view700A. Upon selection of “learn more”button711,view730 is presented atPWD dashboard132. In theexample view730, shown inFIG. 7A, there are six sections,glucose732,sensor use734,average glucose736,low glucose events738,high glucose events740, andrecommendation history742. In one embodiment,recommendation history742 includes history of recommendations selected by an HCP and presented to a PWD.Recommendation history742 may also include recommendations accepted/declined by a PWD. In various embodiments, the information depicted inview730 depicts changes to therapy settings in view of insights and associated recommendations generated byCDS system100A (and/or therapy management system1000).
• FIG. 7B illustrates anexample view700B presented atPWD dashboard132.View700B is configured, generally, to show an insight sent from an HCP.View700B, in various embodiments, is displayed by selecting a request to look at updated information since receiving recommendations from an HCP, in accordance with one or more embodiments of the disclosure. As depicted,view700B includesmessage712 associated with an insight (e.g., blood glucose level is “running low last night”).Message712, in various embodiments, may include real-time information. For example,message712 includes real-time glucose levels generated by a CGM.
• In one embodiment, a PWD selects “learn more”button713. In various embodiments, upon selection of “learn more”button713, an additional view (e.g., view730) is presented atPWD dashboard132 associated with themessage712. Alternatively, upon selection of “learn more”button713, view760 (inFIG. 7D) is displayed that depicts a list of selectable training modules associated with an insight.
• FIG. 7C illustrates anexample summary view700C presented atPWD dashboard132.Summary view700C is configured, generally, to show views at a PWD dashboard of therapy recommendations received from an HCP.Summary view700C includesmessage714.Message714 includes contextual information associated with an insight (e.g., “. . . pattern of high glucose . . . ”). In one embodiment,message714 is an introduction message (e.g., introduction410) drafted/edited by an HCP.
• Upon selection of button716 (e.g., “See Updates” button),view720 is displayed. View720 is provides information related to a contextual information inmessage714. For example,view720 provides information related to RA insulin settings. In particular,view720 provides information related to recommended therapy setting changes associated with RA insulin.
• In various embodiments,view720 includesinsulin name750,meal insulin section752, andcorrection insulin section754.
• Insulin name750 indicates the particular RA insulin used by the PWD (e.g., Novolog).Meal insulin section752 provides a list of meal types (e.g., breakfast, lunch, dinner) and the associated insulin units for each meal type. As depicted, recommended changes to RA insulin therapy includes changes to insulin units associated with the meal type. For example, the recommended therapy change for RA insulin is 4 units for breakfast, 6 units for lunch and 8 units for dinner.
• Correction insulin section754 provides a list of insulin units associated with glucose ranges. For example, the recommended therapy change for RA insulin is 1 unit for a glucose range of 150-200 (mg/dL), 2 units for a glucose range of 201-250, and so on.
• View720 includes “change to”button726 and “change from”button724. Upon selection ofbutton724, the current RA insulin setting are displayed. Upon selection ofbutton726, the recommended therapy changes to RA insulin settings are displayed.
• View720 includes acceptbutton721 and reject (or no)button722. Upon selection ofbutton721, the recommended therapy changes to RA insulin settings (as depicted) are accepted. In one embodiment, the changes are automatically implemented. For example, accepted changes are implemented by therapy management system1000 (which will be described in further detail below). In another embodiment, upon acceptance of therapy change recommendations, an indication of the acceptance by the PWD is transmitted toHCP device140 for display onHCP dashboard142.
• Upon selection ofbutton722, the recommended therapy changes to the RA insulin settings are not accepted (or canceled). In another embodiment, upon selection ofbutton722, an indication of the PWD not accepting the recommended therapy changes is transmitted toHCP device140 for display onHCP dashboard142.
• In various embodiments, display ofview720 is a basic or standard view of statistics associated with insights/recommendations. A PWD may desire more specific information regarding statistics associated with insights/recommendations. For example, summary view700A, inFIG. 7A, depicts additional and more specific statistics associated with insights/recommendations as compared toview720.
• In various embodiments, care notes are presented insummary view700C. For example, care notes are presented inview720 or in another view not shown. The care notes can be created in a rich field text box (not shown) by a PWD. Alternatively, care notes are created by an HCP and presented atPWD dashboard132.
• FIG. 7D illustrates anexample view760 of recommended training modules presented atPWD dashboard132. In various embodiments,view760 is presented to a PWD in response to selection of a “learn more” button (e.g., “learn more”button713, inFIG. 7B). View760 includes one more learning modules associated with a recommendation. For example,view760 depicts three separate (and selectable) training modules (e.g.,training module761,training module762, and training module763). However, in various embodiments,view760 can include any number of selectable training modules.
• The selectable training modules can be in various formats such as a video, document, audio, etc. For example, in response to selection oftraining module761, a video is played to train a PWD. Training modules can include, but is not limited to, (1) benefits of CGM reusable learning content, (2) view CGM troubleshooting reusable learning content and the like.
• FIG. 9 depicts amethod900 of accepting a recommendation by a PWD. Atoperation910,insights engine122 selects a therapy insight associated with the identified predefined behavior. For example, insights engine902 selects “wear sensor more” insight (Table 1, row 1). The “wear sensor more” insight includes one or more recommendations, such as, but not limited to, (1) consider wearing CGM more frequently, (2) consider reviewing a module on the benefits of CGM, and (3) consider reviewing a module on CGM troubleshooting
• Atoperation912,insights engine122 transmits the insight and corresponding recommendation toHCP engine128. Atoperation914, HCP engine129 transmits the insight and corresponding recommendation to HCP device to be presented onHCP dashboard142.
• Atoperation916, an HCP selects one or more recommendations associated with an insight. For example, an HCP selects each of the recommendations associated with the insight (e.g., (1) consider wearing CGM more frequently, (2) consider reviewing a module on the benefits of CGM, and (3) consider reviewing a module on CGM troubleshooting). In various embodiments, the HCP can select a subset or none of the recommendations associated with an insight.
• At operation918, an HCP revises a recommendation. For example, an HCP adds care notes (e.g., care notes440) to the selected recommendation. In various embodiments, an HCP is able to revise a recommendation (already sent to a PWD) when a PWD has not yet accepted or rejected a rejected the recommendation. Atoperation920, an HCP confirms revision of the recommendation (e.g., confirms recommendation with added care notes440).
• Atoperation922, the confirmed recommendation is transmitted fromHCP dashboard142 toHCP engine128. Atoperation924, recommendations (e.g., consider increasing meal dose or changing meal type and monitor glucose ˜2 hours after correction insulin doses delivered, etc.) are then transmitted toPWD device130 and displayed onPWD dashboard132. Atoperation926, recommendations (e.g., consider increasing meal dose or changing meal type and monitor glucose ˜2 hours after correction insulin doses delivered, etc.) are selected and accepted by a PWD.
• Atoperation928, a confirmation of acceptance of recommendation by a PWD is transmitted toHCP engine128. Atoperation930, the confirmation is then transmitted toHCP device140 and presented atHCP dashboard142.
• Some embodiments are directed to a clinical decision support system that operates in conjunction with a therapy management system and vice versa. In general, a therapy management system assists with managing insulin therapy for a PWD (or insulin-based management). For example, aspects of a therapy management system may control delivery of insulin to a patient such as a closed loop insulin delivery system, other aspects may manage settings and/or parameters for such a closed loop insulin delivery system, and other aspects still may include tools for collecting meal and exercise information from a PWD.
• Therapy insights for a patient as well as recommendations selected by an HCP for a patient may be relevant to the operation of a therapy management system. For example, information that a CGM is active <85% of the time or an actual CGM active statistic (e.g., CGM only active 65% of the time) may be relevant to calculations of active insulin available that are based on glucose level data captured by a CGM.
• So, in some embodiments, a therapy management system may be configured to receive therapy insights and/or recommendations and to assist with managing insulin therapy, at least, based in part, on insights and/or recommendations generated byCDS system100A.
• FIG. 10 depicts an embodiment oftherapy management system1000 communicatively coupled withCDS system100.Therapy management system1000 may be part ofCDS system100A, or it may be separate. In some cases, it is expected that there will be some overlap between elements oftherapy management system1000 andCDS system100A. For example,PWD device130 may run an application forPWD dashboard132 and run a therapy management application (not shown). Additionally,therapy management system1000 may communicate withCDS system100A (or any device/engine ofCDS system100A) viacommunication network160.
• Therapy management system1000, in various embodiments, may be configured, generally, to assist with managing insulin therapy for a patient. More specifically,therapy management system1000 is configured to provide information about insulin therapy and provide therapy recommendations based on blood glucose data and insulin dosing data for a patient. In a case of manual delivery systems and open-loop delivery systems,therapy management system1000 may be configured primarily to provide information and recommendations to a patient. In a case of closed-loop delivery systems,therapy management system1000 may be configured to automatically adjust some therapy settings and user parameters (e.g., insulin sensitivity) based on blood glucose data, insulin dosing data, physiological data, and more.
• In some embodiments,therapy management system1000 may be configured to provide (e.g., report) a patient's therapy data toCDS system100A thatCDS system100A utilizes to provide therapy insights and therapy recommendations. For example, referring toFIG. 10,therapy management system1000 may be one of theexternal resources150.
• Therapy management system1000 may receive or have access to some or all of the insights and/or recommendations provided byCDS system100A based on the therapy data ofPWD1105. In one embodiment, only the therapy insights related to managing insulin therapy might be provided totherapy management system1000, for example, an insight that a PWD did not wear her CGM during a certain time period. In another embodiment, only recommendations for specific therapy settings or therapy changes are sent totherapy management system1000.
• For example, referring toFIG. 4, recommendations420 (selected by an HCP and sent to a PWD) are associated with recommended setting changes430. Recommended setting changes430 may be sent totherapy management system1000, andtherapy management system1000 may, based on its settings, determine whether to change a current therapy settings based on one or more of recommended setting changes430. In one embodiment,therapy management system1000 may simply reject the recommendation because a discretionary setting at thetherapy management system1000 is set too low to allow thetherapy management system1000 to change therapy settings in general or to change the specific therapy setting. Alternatively or in addition,therapy management system1000 may model glucose response for a PWD if the therapy setting is changed based on the recommendation, and then determine whether to change the setting based on the modeled glucose response of the patient.
• FIG. 11A depicts an embodiment oftherapy management system1100A. Therapy management system1100 includes aglucose sensor1110,delivery system1115 andmobile device1120.
• Glucose sensor1110 may be any suitable glucose sensor system, such as a blood glucose meter (BGM) adapted to determine blood glucose values using blood glucose test strips, and flash glucose monitor, or a CGM. In some cases,glucose sensor1110 may be configured to act as both a flash glucose monitor and a continuous glucose monitor by permitting both intermittent and on-demand transmissions of blood glucose data. In some embodiments,glucose sensor1110 can wirelessly transmit data when interrogated by a reader device (e.g., using NFC communication). In some embodiments,glucose sensor1110 can wirelessly transmit data at predetermined intervals (e.g., using radio frequencies) using any suitable communication standard (e.g., Bluetooth Low Energy (BLE)). In some cases, systems and methods provided herein can include multiple glucose sensor systems (e.g., a continuous or flash glucose monitor and a blood glucose meter).
• In some embodiments,glucose sensor1110 can transmit glucose data using multiple communication techniques. In some embodiments,mobile device1120 and/ordelivery system1115 may include an NFC reader adapted to obtain blood glucosedata glucose sensor1110 when brought within an interrogation distance ofglucose sensor1110. In some embodiments,glucose sensor1110 broadcasts blood glucose data at predetermined periods of time (e.g., every 30 seconds, every minute, every 2 minutes, every 3 minutes, every 5 minutes, every 10 minutes, every 15 minutes, etc.).
• In a polled (or interrogated) mode of operation,glucose sensor1110 may wirelessly send blood glucose data to one or more ofmobile device1120 anddelivery system1115 that corresponds to a historical period. For example, whenglucose sensor1110 is interrogated,glucose sensor1110 may send stored glucose data from the previous 1 hour, 2 hours, 3, hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, etc. In some cases, broadcast blood glucose data may only include a current or more recent blood glucose value. For example, in some cases blood glucose data may include only the most current readings (e.g., from the last 10 minutes).
• Glucose sensor1110 can transmit glucose data toCDS system100A and stored indata store104, as described herein. In one embodiment,glucose sensor1110 transmits glucose data tomobile application1125 ofmobile device1120. Upon receipt,mobile application1125 transmits the glucose data (e.g., blood glucose data108) todata store104 ofCDS system100A vianetwork1130. In one embodiment,network1130 is the same asnetwork160.
• Mobile application1125, in various embodiments, may execute on any suitable mobile computing device that can store and execute a mobile application that is adapted to display and input therapy relevant information wireless sly received from the other components of the system as well as from a graphical user interface that enables user to interact with the application. In one embodiment,mobile device1120 can also store and execute a trusted mobile application within a trusted execution environment (hardware and/or software) that is not, generally speaking, accessible to users or devices communicating withmobile device1120 but that is accessible to other applications executing onmobile device1120. Various functions and calculations that relate to the therapy management system, including alerts and recommendations that are presented to users may be, in part or in whole, performed by the trusted mobile application. Moreover, some or all communication withdelivery system1115, such as, but not limited to,delivery system1115,glucose sensor1110, and, and other accessories may be restricted to the trusted mobile application.
• As described herein, a PWD receives recommendations from an HCP. The recommendations may be displayed on a PWD dashboard (e.g., PWD dashboard132) ofmobile device1120 viamobile application1125. Upon acceptance of a recommendation,therapy management engine1140 identifies one or more recommended therapy settings that are associated with a selected recommendation.
• In one embodiment,therapy management engine1140 automatically adjusts a therapy setting with the identified recommended therapy setting. For example,therapy management engine1140 adjusts a current therapy setting of a large meal dose of insulin of 5 units to a large meal dose of insulin of 4 units.
• Therapy management engine1140 transmits the adjusted therapy setting tomobile application1125 ofmobile device1120. In one embodiment, responsive to receiving the adjusted therapy setting,mobile application1125 automatically implements the adjusted therapy setting. For example,mobile application1125 provides instructions todelivery system1115 such thatdelivery system1115 implements the adjusted therapy setting.
• In one embodiment, upon automatic implementation of the adjusted therapy setting,mobile application1125 displays an indication to a PWD that a therapy setting has been changed to the adjusted therapy setting. In another embodiment,mobile application1125 does not indicate to a PWD that a therapy setting has been changed.
• In another embodiment,mobile application1125 displays the adjusted therapy setting for acceptance by a PWD. For example,mobile application1125 prompts a PWD to accept the adjusted therapy setting. Upon acceptance, mobile application implements the adjusted therapy setting, as described above.
• FIG. 11B depicts an embodiment oftherapy management system1100B.Therapy management system1100B includesinsulin delivery system1150. In various embodiments,insulin delivery system1150 includes is an open loop system or a closed loop system. An open loop system involves a PWD administering insulin to his or herself, for example, via an insulin pen. A closed loop system (also referred to as an artificial pancreas) is a system that monitors blood glucose levels automatically and provides insulin to a PWD. For example, a CGM monitors blood glucose levels of a PWD and an insulin pump (communicative coupled to the CGM) delivers insulin to the PWD. Additionally, in some embodiments, insulin delivery system includes a mobile device (e.g., PWD device130).
• Therapy management system1100B includesPWD engine126,HCP engine128, insights/recommendation engine1160 andtherapy management engine1140. In various embodiments, insights/recommendation engine1160 includesHCP engine128 andrecommendation engine124. Insights/recommendation engine1160, in various embodiments, receivesPWD data106insulin delivery system1150 and/ortherapy management engine1140. For example,therapy management engine1140 receivesPWD data106 frominsulin delivery system1150. Upon receivingPWD data106 frominsulin delivery system1150,therapy management engine1140 transmitsPWD data106 to insights/recommendation engine1160.
• Insights/recommendation engine1160 generates insights and/or recommendations, as described herein, based, at least in part, on data frominsulin delivery system1150 and/ortherapy management engine1140. In various embodiments, insights may be generated without knowing the amount of insulin delivered. For example, the insights may be generated relying on system recommended dosing (i.e. meal & correction vs just meal). In such an example, insights/recommendation engine1160 generates insights/recommendations based on rules to distinguish which insulin and direction to change (e.g., RA meal increase, RA correction decrease, and LA decrease).
• Embodiments of therapy management system depicted inFIGS. 11A-B, and methods of configuring and operating a therapy management system, may be performed, in whole or in part, in cloud computing, client-server, or other networked environment, or any combination thereof. The components of such a system may be located in a singular “cloud” or network, or spread among many clouds or networks. End-user knowledge of a physical location and/or configuration of components of a system are not required.
• FIG. 12 illustrates a method1200 for managing therapy settings for a PWD. In various embodiments, method1200 is implemented, at least based in part, bytherapy management system1000 and/or therapy management system1100. In various embodiments, method1200 is implemented, at least based in part, byCDS system100A.
• Atoperation1210, an acceptance of a behavior recommendation associated with a therapy insight sent to a user-dashboard is received. For example, one or more behavior recommendations are presented at a PWD dashboard. A user selects one or more of the behavior recommendations that are presented at a PWD dashboard.
• Atoperation1220, a recommended therapy setting associated with the accepted behavior recommendation is identified. Upon selection of a recommendation, in one embodiment,therapy management engine1140 identifies a recommended therapy setting associated with the accepted behavior recommendation. For example, referring toFIGS. 5 and 11,therapy management engine1140 identifies a recommended setting change531 associated with one or more of selected recommendations420 (selected by a PWD).
• Atoperation1230, a therapy setting to the recommended therapy setting is adjusted. In one embodiment, referring once again toFIGS. 5 and 11,therapy management engine1140 adjusts a first therapy setting, such as a large meal insulin dose of 5 units (e.g. a scheduled dose) to a second, different therapy setting of a large meal insulin dose of 4 units.
• In another embodiment,therapy management engine1140 sends a recommended therapy setting tomobile application1125 that displays the recommended therapy setting. Upon selection of the recommended therapy setting by a PWD,mobile application1125 adjusts a therapy setting to the recommended therapy setting.
• Atoperation1240, the adjusted therapy setting is sent to the user-dashboard. For example, upon a therapy setting adjusted to a recommended therapy setting (e.g., a large meal insulin dose of 5 units is adjusted to a dose of 4 units), the adjusted therapy setting is sent toPWD dashboard132.
• FIG. 13 illustrates amethod1300 for managing therapy settings for a PWD.
• Atoperation1310,therapy management engine1140 identifies a recommended therapy setting. For example, a PWD selects a behavioral recommendation provided by a HCP.Therapy management engine1140 then identifies a recommended therapy setting (e.g., reducing correction insulin by 20%) associated with the behavioral recommendation.
• At operation1315, upon identifying the recommended therapy setting,therapy management engine1140 adjusts the therapy setting. For example,therapy management engine1140 adjusts a current therapy setting of correction insulin (e.g., 10 units) to an adjusted therapy setting (e.g., 8 units).
• At operation1315,therapy management engine1140 sends the adjusted therapy setting tomobile application1125.
• At operation1325,mobile application1125 transmits the adjusted therapy setting todelivery system1115 for implementation of the adjusted therapy setting by the delivery system (e.g., providing 8 units of correction insulin to a PWD). In one embodiment,mobile application1125 sends the adjusted therapy setting to delivery system upon acceptance of the adjusted therapy setting by a PWD. In another embodiment,mobile application1125 sends the adjusted therapy setting to delivery system without prompting a PWD for acceptance of the adjusted therapy setting.
• At operation1330,mobile application1125 indicates to a PWD that a therapy setting has been adjusted.
• At operation1335, delivery system1115 (e.g., insulin pump) operates according to the adjusted therapy setting. For example,delivery system1115 injects a PWD with insulin according to the adjusted therapy setting (e.g., 8 units of correction insulin).
• The embodiments described herein may include the use of a special-purpose or general-purpose computer including various computer hardware or software modules, as discussed in greater detail below.
• Embodiments described herein may be implemented using computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media may be any available media that may be accessed by a general-purpose or special-purpose computer. Special-purpose computer is intended to be interpreted broadly and encompasses embedded systems, microcontrollers, application specific integrated circuits, digital signal processors, and general-purpose computers programmed for specific purposes. Segments (e.g., code segment or data segment) may refer to a portion (e.g., address) of memory, virtual memory, or an object file.
• By way of example, and not limitation, such computer-readable media may include non-transitory computer-readable storage media including Random Access Memory (RAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory devices (e.g., solid-state memory devices), or any other storage medium which may be used to carry or store desired program code in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computer. Combinations of the above may be included within the scope of computer-readable media.
• Computer-executable instructions comprise, for example, instructions and data which cause a general-purpose computer, special-purpose computer, or special-purpose processing device (e.g., one or more processors) to perform a certain function or group of functions. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
• Any ranges expressed herein (including in the claims) are considered to be given their broadest possible interpretation. For example, unless explicitly mentioned otherwise, ranges are to include their endpoints (e.g., a range of “between X and Y” would include X and Y). Additionally, ranges described using the terms “approximately” or “about” are to be understood to be given their broadest meaning consistent with the understanding of those skilled in the art. Additionally, the terms “approximately” or “substantially” include anything within 10%, or 5%, or within manufacturing or typical tolerances.
• The features of the various embodiments described herein are not mutually exclusive and can exist in various combinations and permutations, even if such combinations or permutations are not expressly described herein, without departing from the scope of the disclosure. In fact, variations, modifications, and other implementations of what is described herein will occur to one of ordinary skill in the art without departing from the scope of the disclosure. As such, the invention is not to be defined only by the preceding illustrative description, but only by the claims which follow, and legal equivalents thereof.

Claims (35)

1. A computer-implemented method for automated provisioning of clinical advice to a provider caring for a patient with diabetes (PWD), comprising:

detecting a clinically relevant pattern in insulin therapy data of a patient under the care of a provider;

identifying a predefined behavior of a PWD responsive to the detected clinically relevant pattern, wherein the clinically relevant detected pattern is indicative of desirable behaviors and/or undesirable behaviors related in perspective of insulin-based management of diabetes; and

selecting a therapy insight associated with the identified predefined behavior, wherein the therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation; and

responsive to the selecting the therapy insight, automatically sending, to a provider-dashboard associated with the provider caring for the PWD, the selected therapy insight.

2. The computer-implemented method ofclaim 1, wherein sending the selected therapy insight comprises:

sending one or more of a behavior recommendation to adjust the undesirable behaviors and a behavior recommendation to continue the desirable behaviors.

3. The computer-implemented method ofclaim 1, wherein sending the selected insight comprises:

sending one or more of a long acting dosing recommendation and a rapid acting dosing recommendation.

4. The computer-implemented method ofclaim 1, wherein identifying a predefined behavior of a PWD further comprises:

identifying whether one or more of a scheduled dose of insulin was administered to the PWD and an incorrect dose of insulin was administered to the PWD.

5. The computer-implemented method ofclaim 1, further comprising:

correlating therapy insights to associated health risks; and

prioritizing the therapy insights based on a severity of the health risks.

6. The computer-implemented method ofclaim 1, further comprising:

prioritizing therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame.

7. The computer-implemented method ofclaim 1, further comprising:

responsive to determining that the insulin therapy data does not correspond to a clinically relevant pattern, forgo identifying the predefined behavior.

8. The computer-implemented method ofclaim 1, wherein the insulin therapy data is selected from a group consisting of: glucose data, therapy settings and insulin dosing data.

9. The computer-implemented method ofclaim 1, wherein the insulin therapy data is glucose measurements continuously received, wherein at least some of the glucose measurements includes date, time and glucose value.

10. The computer-implemented method ofclaim 1, wherein the insulin therapy data is dosing information that includes date, time and amount of a dose of insulin.

11. The computer-implemented method ofclaim 1, further comprising:

receiving selection of one or more behavior recommendations associated with the therapy insight sent to the provider-dashboard.

12. The computer-implemented method ofclaim 1, further comprising:

receiving an indication of cancelation of one or more behavior recommendations associated with the therapy insight sent to the provider-dashboard.

13. The computer-implemented method ofclaim 1, further comprising:

receiving adjustments of the therapy insight sent to the provider-dashboard.

14. A system for automated provisioning of clinical advice to a provider caring for a patient with diabetes (PWD) comprising:

an insights engine configured to:

detect a clinically relevant pattern in insulin therapy data of a patient under the care of a provider;

identify a predefined behavior of a PWD responsive to the detected clinically relevant pattern, wherein the clinically relevant detected pattern is indicative of desirable behaviors and/or undesirable behaviors related in perspective of insulin-based management of diabetes; and

select a therapy insight associated with the identified predefined behavior, wherein the therapy insight comprises clinical advice associated with the PWD that includes a behavior recommendation; and

a health care provider (HCP) engine configured to automatically send, to a provider-dashboard associated with the provider caring for the PWD, the selected therapy insight.

15. The system ofclaim 14, wherein the HCP engine is further configured to send one or more of a behavior recommendation to adjust the undesirable behaviors and a behavior recommendation to continue the desirable behaviors.

16. The system ofclaim 14, wherein the HCP engine is further configured to send one or more of a behavior recommendation to adjust the undesirable behaviors and a behavior recommendation to continue the desirable behaviors.

17. The system ofclaim 14, wherein the HCP engine is further configured to receive selection of one or more behavior recommendations associated with the therapy insight sent to the provider-dashboard.

18. The system ofclaim 14, wherein the HCP engine is further configured to receive an indication of cancelation of one or more behavior recommendations associated with the therapy insight sent to the provider-dashboard.

19. The system ofclaim 14, wherein the HCP engine is further configured to receive adjustments of the therapy insight sent to the provider-dashboard.

20. The system ofclaim 14, wherein the insights engine is further configured to identify whether one or more of a scheduled dose of insulin was administered to the PWD and an incorrect dose of insulin was administered to the PWD.

21. The system ofclaim 14, wherein the insights engine is further configured to:

correlate therapy insights to associated health risks; and

prioritize the therapy insights based on a severity of the health risks.

22. The system ofclaim 14, wherein the insights engine is further configured to prioritize therapy insights based on frequency of occurrences of the predefined behavior during a predetermined time frame.

23. The system ofclaim 14, wherein the insights engine is further configured to responsive to determining that the insulin therapy data does not correspond to a clinically relevant pattern, forgo identifying the predefined behavior.

24. The system ofclaim 14, wherein the insulin therapy data is selected from a group consisting of: glucose data, therapy settings and insulin dosing data.

25. The system ofclaim 14, wherein the insulin therapy data is glucose measurements continuously received, wherein at least some of the glucose measurements includes date, time and glucose value.

26. The system ofclaim 14, wherein the insulin therapy data is dosing information that includes date, time and amount of a dose of insulin.

27. A computer-implemented method for managing insulin therapy settings for a patient with diabetes (PWD), comprising:

receiving an acceptance of a behavior recommendation associated with a therapy insight sent to a user-dashboard, wherein the user-dashboard is associated with a PWD;

identifying a recommended insulin therapy setting associated with the accepted behavior recommendation;

adjusting an insulin therapy setting to the recommended insulin therapy setting; and

sending the adjusted insulin therapy setting to the user-dashboard.

28. The computer-implemented method ofclaim 27, wherein the sending the adjusted insulin therapy setting to the user-dashboard is to automatically adjust the insulin therapy setting to the recommended insulin therapy setting.

29. The computer-implemented method ofclaim 27, wherein the sending the adjusted insulin therapy setting to the user-dashboard is to be displayed on the user-dashboard.

30. The computer-implemented method ofclaim 27, further comprising:

operating an insulin delivery system with the adjusted insulin therapy setting.

31. A computer-implemented method for managing insulin therapy settings for a patient with diabetes (PWD), comprising:

receiving an acceptance to adjust an insulin therapy setting displayed on a user-dashboard associated with a PWD, wherein the insulin therapy setting is associated with a behavior recommendation for the PWD;

automatically adjusting the insulin therapy setting corresponding to the accepted adjusted insulin therapy setting; and

sending the adjusted insulin therapy setting to the user-dashboard.

32. The computer-implemented method ofclaim 31, further comprising:

operating an insulin delivery system with the adjusted insulin therapy setting.

33. A computer-implemented method for automated provisioning of clinical advice to a provider caring for a patient with diabetes (PWD), comprising:

identifying, by an insights engine associated with a first computing system, a predefined behavior of a PWD responsive to a detected clinically relevant pattern in insulin therapy data;

selecting, by the insights engine, a therapy insight associated with the identified predefined behavior; and

responsive to the selecting the therapy insight, automatically sending over a network, to a health care provider engine associated with a second computing system separate and distinct from the first computing system, the selected therapy insight, wherein the health care provider engine is configured to automatically send the therapy insight to a provider-dashboard associated with a provider caring for the PWD.

34. The computer-implemented method ofclaim 33, further comprising:

detecting the clinically relevant pattern in insulin therapy data of a patient under the care of a provider.

35. The computer-implemented method ofclaim 33, further comprising:

receiving a selected therapy insight from the health care provider engine; and

transmitting the selected therapy insight to a PWD dashboard.

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